My Projects

• Precision Agriculture

  In the field of precision agriculture, I worked on a project that utilized data analytics to enhance farming practices. By analyzing key agricultural data points such as soil pH, temperature, humidity, rainfall, and NPK levels, the project provided actionable insights to help farmers optimize crop yield, reduce costs, and improve sustainability. Through machine learning models, we enabled predictions for optimal planting times, detection of nutrient deficiencies, and recommendations for irrigation and fertilization. Real-time field monitoring, integrated with weather data and soil conditions, allowed farmers to adjust practices for improved efficiency and sustainability.

  
• Flower Classification Web Application

  I developed a Flower Classification Web Application using machine learning to predict flower species based on attributes like sepal and petal dimensions. The application, built with the Iris dataset, demonstrates supervised learning, feature engineering, and model evaluation. Users can upload measurements and receive real-time species predictions, showcasing the practical use of machine learning in botany.

• Salary Prediction Model

  The Salary Prediction Model is a data-driven project that predicts salaries based on factors like age, gender, education, job title, and experience. Using machine learning algorithms such as linear regression and random forests, the model provides insights into salary trends across industries. It helps job seekers make informed career decisions and assists businesses in optimizing compensation strategies to stay competitive.

Analysis Report: Predicting Housing Sale Prices Using Machine Learning and R

The real estate market is influenced by various factors such as property features, location, and market trends. Accurate prediction of housing sale prices is crucial for both buyers and sellers. This analysis uses machine learning techniques in R to predict housing sale prices based on a dataset of housing transactions. The primary objective is to identify key predictors of sale price.

The R project revealed that total living area and structure quality are the most significant predictors of housing sale prices, with location-based factors like proximity to highways and subcenters also influencing values. Geographic analysis highlighted the impact of location on property prices.

Click the link below to view the analysis: